To effectively use the complementarity of different keyword spotting systems and solve the problem that the confidence scores from several different subsystems is not in the same range, a keyword spotting system based on score normalization and system combination is proposed. Firstly, to avoid keyword missing due to pruning errors in a large vocabulary recognition system, the keyword soft Beam pruning method is presented. Secondly, score normalization is needed to transform these confidence scores into a common domain, prior to combining them. Finally, after score normalization，the outputs are combined from different subsystems. Results show that score normalization methodology improves keyword search performance by 30% in average. Experiment also show that combining the outputs of diverse systems, system perform is 10% better than the best normalized KWS system.